Measuring the competitive performance of the South African stone fruit

industry

Johann Du Toit Loubser Boonzaaier¹ & Johan Van Rooyen²

¹Bureau for Food and Agricultural Policy (BFAP) & Department of Agricultural Economics,

University of Stellenbosch, South Africa

JS Marais Building, Private Bag X1, Matieland, Stellenbosch, 7602

E-mail address: 13700790@sun.ac.za, Phone number: +27822166660

²Bureau for Food and Agricultural Policy (BFAP) & Director of The Centre for Agribusiness

Leadership, University of Stellenbosch, South Africa

JS Marais Building, Private Bag X1, Matieland, Stellenbosch, 7600

E-mail address: cjvr@sun.ac.za, Phone number +827853300

Measuring the competitive performance of the South African stone fruit

industry value chain.

Abstract

The aim of the study is to enquire about the global competitive performance of the South

African stone fruit industry for the period 1961 - 2012. A five-step analytical framework was

applied, using approaches by Balassa (revealed comparative advantage, RCA), Vollrath

(relative trade advantage, RTA) and the Porter Diamond Model, together with statistical

methods such as cluster analyses, principle component analyses and Cronbach’s alpha, to

reflect differences in stakeholder opinions and views within the value chain. South African

stone fruit was found to perform consistently at high, but fluctuating competitive levels in

highly contested global markets (RTA values of between 0.41 and 5.61 since 1961);

particularly since the economic deregulation of the industry in the mid 1990’s. A strategic

planning framework was drafted with participation from key industry role-players,

identifying eleven industry level strategic focus areas. These included improved strategic

communication within the value chain and improved industry intelligence systems.

Key words: competitive performance, RTA, Porter Diamond, industry value chain, executive

survey, South African stone fruit industry

Introduction, research questions and objectives 1.

The economic growth of the Republic of South Africa’s agricultural sector is of considerable

importance to the country’s realisation of key economic and development objectives

(National Planning Commission, 2011; Strauss, Meyer & Kirsten, 2010). For this reason,

competitiveness must be viewed as an important feature and it was indeed identified as one of

the cornerstones of South African agricultural policy – in the Agricultural Sector Plan (2001)

and, more recently, in the National Development Plan (National Planning Commission,

2011). This point is also argued extensively by Esterhuizen (2006), Van Rooyen, Esterhuizen

and Stroebel (2011), and Van Rooyen and Esterhuizen (2012). As the Republic of South

Africa today functions as an integral part of the global market-orientated economy.

A higher level of global competitiveness is thus essential to operate successfully in this

environment by trading more efficiently and effectively, with better quality products at

strategically selected price points, produced through more productive practices (Smit,

2010).One of the major challenges that international agribusinesses are facing is the ability to

remain, maintain, and enhance competitive performance.

Research questions: the major research questions that focussed this study are

How should competitiveness be defined and measured of an industry that show high

trade in highly competitive global markets; does the unequal playing fields in this

environment influence competitive performance- government policies, trade barriers,

differing supporting industries, etc.?

How is long term trends determined and analysed?

How do you enquire about views and opinions of industry decision makers along the

value chain?

How do you create a systemic set of determinants capturing the varying factors

enhancing and constraining competitive performance?

How can a strategic framework for industry level action be established

This paper is set to deal with these questions in order to inquire and pronounce about the

global competitive performance of the South African stone fruit1 industry, comparing

different levels in the value chain, and aiming to set a framework to enhance the over-all

competitive performance of the industry as a whole.

The objectives of this study are:

Define the competitive performance of the South African stone fruit industry in the

global context.

Conduct a comprehensive empirical measurement for the competitive performance of

the South African stone fruit industry over time.

Determine the wider set of rudiments/factors that affect the competitiveness of the

South African stone fruit industry.

Analyse such factors and establish the major determinants affecting competitive

performance.

1 Refers to members of the genus Prunus, namely plums (Prunus salicina L., Japanese plum), peaches (Prunus

persica), nectarines (Prunus nucipersica), apricots (Prunus armeniaca), prunes – often called sloes (Prunus

domestica L, European plum), cherries (Prunus avium) and almonds (Prunus amygdalus)

Propose industry-level strategies and institutional incentives to support and augment

the level of competitiveness of the South African stone fruit industry.

This study used an enquiry system using both qualitative and quantitative methods through a

step-wise analytical framework:

Step 1: Defining competitiveness as applicable to a highly traded commodity in a highly

contested global market

Step 2: Measuring the competitive status and performance of the South African stone fruit

industry over a long term time period

Step 3: Identify factors affecting – constraining and enhancing - the competitive

performance of the South African stone fruit industry.

Step 4: Establish sets of the major determinants of industry competitiveness

Step 5: Propose strategies to enhance the competitiveness of the South African stone fruit

industry

The SA stone fruit industry in a nutshell 2.

The stone fruit industry in South Africa has come a long way since the establishment of the

deciduous fruit industry in the 17th century. Due to the difference in chilling requirements2

for stone fruit cultivars to perform under normal conditions, stone fruit are produced in all

nine provinces, but mainly produced in the Western Cape. South African stone production

roughly 1% of the global stone fruit produced and when compared within the southern

hemisphere South Africa produces roughly 16%. According to HORTGRO (2014a) there are

330 402 ton of stone fruit produced on 18 098 ha cultivated by 1 058 production units. The

value of the collective South African stone fruit industry’s contribution to the South African

economy indicated steady upsurges from the 2005/2006 season to a total value of $ 200.35

million3 for the production season (2012/13).From a social/ethical point of view, the stone

fruit industry employs, at the primary production farm level, roughly 24 000 labourers,4 who

have approximately 95 000 dependants HORTGRO (2014a). Most of the economic activities

kick-started by the primary production of stone fruit, not only in the immediate production

vicinities, but also right up the value chain and down the supply chain, give rise to

employment opportunities and business ventures that are created, thereby adding stability to

local economies.

The focus of this research was on the stone fruit export segment, which grew significantly

post-deregulation in 1997 as this segment fundamentally affects the competitiveness in the

milieu of key measurements applied to assess the competitiveness of this related area and

industry of interest. During 2002/03 56 184 ton was exported and during 2012/13 76 462 ton

were exported. South African stone fruit trades successfully in the highly traded global

market, with a share of 2.23% of the world stone fruit volume and of 14.75% of Southern

Hemisphere stone fruit value According to HORTGRO (2014a), the total stone fruit export

value – collectively apricots, plums (including prunes), peaches, nectarines and cherries –

amounted to a monetary value of $ 103.39 million5 during the 2012/2013 production/export

2 Measured in Infruitec units: Very low < 250; Low 250-400; Medium 400-800; High > 800 (HORTGRO,

2014b).

3 ZAR 1 761.03 million (South African Rand) given an exchange rate to the US $ for January 2013.

4 Permanent equivalent (seasonal labour converted to permanent equivalent)

5 ZAR 908.79 million (South African Rand) given an exchange rate to the US $ for January 2013

season, representing a contribution of 51.61% to the total value of South African stone fruit

production, although 23.14% of this total volume produced was exported during the same

period, which indicates a dynamic trading environment. The major stone fruit export

destinations per type are shown in Table 1.

Table 1. Export destinations for the 2012/2013 production season.

Stone fruit type Middle

East

EU &

Russia

United

Kingdom

Far

East &

Asia

Indian

Ocean

Islands

Africa USA &

Canada

Apricots 31% 47% 21% 1% < 1% < 1% < 1%

Peaches 43% 13% 36% 1% 5% 2% < 1%

Nectarines 27% 19% 47% 2% 2% 2% 1%

Plums & prunes 16% 52% 25% 5% 1% 1% < 1%

Cherries 54% 0% 35% 5% 0% 6% 0%

Source: Adapted from Boonzaaier (2015)

When it comes to the analysis of stone fruit exports, plums clearly dominate the local scene,

with a share of 77.94% in the 2012/2013 season as shown in Figure 1. However, the joint

attributed value of peaches, nectarines, apricots and cherries cannot be left unrecognised.

Figure 1. Stone fruit exported volume per stone fruit type

Source: Boonzaaier (2015)

The notion of South African agricultural value chains by Ortmann (2001) fundamentally

affects and has effects on the competitiveness performance of industries. The supply and

value chain is a complex linkage of various production and operational (and strategic)

stakeholders6 with consolidations on certain activities and internal competition/rivalry.

(DAFF, 2012). To shape an improved understanding of the stone fruit industry’s challenges

6 Stakeholders include, among others, producer organisations, financial institutions, exporters/marketers,

logistical service providers, cultivar developers/breeders, producers, input suppliers, consumers, etc.

2002/03 2003/04 2004/05 2005/06 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13

Apricot 4638 5376 3501 5326 3114 3865 4922 4637 4190 6097 5197

Peaches & Nectarines 9535 7740 7477 5524 6745 7416 7359 9350 8815 11009 11639

Plums & Prunes 41922 44109 39633 27906 40689 46639 46713 41353 49331 50014 59593

Cherries 89 233 19 12 12 14 59 51 70 56 33

Total Exports 56184 57458 50630 38768 50560 57934 59053 55391 62406 67176 76462

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

Exp

ort

Vo

lum

es

(to

n)

and opportunities, the value chain guides the inclusion of key decision makers’ perceptions

across various disciplines of issues surrounding competitiveness to be included in the Stone

Fruit Executive Survey (SFES).

Literature review, concepts and definitions 3.

3.1 Defining competitiveness (Step 1 of the analytical framework)

The term “competitiveness” originated from the classical Latin word petere, meaning to seek,

attack, aim at, and the Latin prefix con-, meaning together. Banterle (2005) characterises the

competitiveness of a particular industry, such as the food sector, as meaningful by

considering economic theory references and, subsequently, the sources of the competitiveness

concept.

Competitiveness can be defined on various levels and from various points of view.

Nevertheless, it was of the utmost importance that an appropriate and unequivocal definition

of competitiveness be adopted within an agricultural trade framework to apply an applicable

and valid measure to be utilised as a proxy for the evaluation of competitiveness. Esterhuizen

(2006) highlights that competitiveness is a tool to exploit and investigate the local and/or

global market reality for relative gains from trade compared to other competitors. Ideas were

also drawn from Gittinger (1982), Freebairn (1987), Van Rooyen (2008) and Jafta (2014)

Competitiveness in this study is defined as the “sustained ability of the South African stone

fruit industry to attract investment by trading its produce competitively within the global

marketplace, whilst continuously striving to earn returns greater that the opportunity cost of

scarce resources engaged”. This definition gives effect to competing in a highly contested the

global trade environment, focusing on “competitiveness advantage” rather than “comparative

advantage” analytical viewpoints (Porter, 1998; Esterhuizen, 2006; Boonzaaier, 2015).

3.2 Measuring competitiveness

From the above definition with actual trade as a central component of competitive

performance, the need to measure trade and trends over time empirically is apparent.

Competitiveness as defined above emphasises the “ability to trade; and to sustain/enhance

such performance”

Industry boundaries are defined and accepted and the competitive rules of the game are

known in the ‘market space’, where entities try to outperform their rivals for a greater share

of existing demand. Entities that understand the ‘rules of engagement’ and play the game of

‘out-trading’ the best will be frontrunners to compete for resources across nations and

economic sectors. Thus it always will be important to navigate successfully in this market

space by outcompeting the rivals, as an entity’s ability to trade is viewed as the foundation of

competitive performance (Freebairn, 1987; Kirsten, 1999; Kim & Mauborgne, 2005a; Farole,

Reis & Wagle, 2010).

3.2.1 Revealed comparative advantage and derived indicators

The concepts of competitive advantage as the basis for the measurement of competitiveness

were advanced by Balassa (1965; 1977) in terms of the revealed comparative advantage

(RCA), reflecting the “ability to trade” despite various nation based government interventions

and other market distortions often contravening comparative advantage positions. The

Balassa view thus deals with “real” world trade and not “what ought to be traded”. In the

literature on the RCA it is often referred to as the Balassa index.

Vollrath (1991) offered an extended specification of RCA, avoiding potential “double

counting” viz the relative trade advantage (RTA7), which takes both imports and exports into

account as a more comprehensive indicator of revealed comparative advantage, is calculated

as the difference between relative export advantage (RXA), which is equal to the Balassa

index, and its counterpart, the relative import advantage (RMA). RTA is formulated

accordingly. See footnote 7.

The RXA measure, which is grounded in exports, calculates the ratio of a nation’s export

share of a commodity in the international market to the nation’s export share of all other

commodities. A RCA index greater than 1 indicates that the country has a comparative

advantage in the commodity under consideration. Hence it reveals a higher state of

competitiveness, since it has a strong export sector. Vollrath (1991) proposed the RMA

index, which is similar to the above-mentioned RXA, but relates to imports (M), rather than

exports. By considering both imports and exports, the RTA indicator implicitly weighs the

revealed competitive advantage by calculating relative export and relative import competitive

advantages. Therefore it is not dominated by extremely small export or import values for the

commodity “measured”, which means that this RTA is a more wide-ranging (all-inclusive)

and superior measure of competitiveness (Esterhuizen, 2006).

3.1.1 Export- and import-related measures of competitiveness

Comparative advantage proposes that trade flows are the result of differences in production

factors/endowments among countries and that a country will specialise in the production of a

good in which it has a cost advantage (Latruffe, 2010; Pugel, 2012).

In addition to the RCA and RTA, other trade-based measures are also applied in the

assessment of competitiveness as presented in Table 2. However, it must be noted that these

measures/indicators have a very direct/restricted focus, taking only specific factors into

consideration, and may be viewed as somewhat limited and less-encompassing in the context

of the global analysis in this study.

Table 2. Summary of additional trade-based measures of competitiveness

Measuring

techniques Description Author(s) applicable

Real exchange

rate (RER)

The ratio of the price index of tradeable commodities to that of non-

tradeable inputs. Where the demand for the currency of a competitive

nation is high, the nation’s exchange rate is strengthened – so for the

inverse too.

Brinkman (1987) and

Frohberg and Hartmann

(1997)

7 𝑹𝑻𝑨𝒊𝒋 = 𝑹𝑿𝑨𝒊𝒋 - 𝑹𝑴𝑨𝒊𝒋

for the i-th nation and j-th commodity, where a positive value of RTA reflects a status of competitive advantage.

𝑹𝑪𝑨𝒊𝒋 = 𝑹𝑿𝑨𝒊𝒋 =[𝑿𝒊𝒋

𝑿𝒊𝒌] / [

𝑿𝒏𝒋

𝑿𝒏𝒌]

where X are exports, k denotes all commodities other than j, and n denotes all other countries than i.

𝑹𝑴𝑨𝒊𝒋 =[𝑴𝒊𝒋

𝑴𝒊𝒌] / [

𝑴𝒏𝒋

𝑴𝒏𝒌]

In this case, a RMA index of less than 1 indicates revealed comparative advantage and thus higher

competitiveness.

Purchasing power

parities (PPP)

A measure for comparing different countries’ relative prices. The number

of units8 in the domestic currency that would be required to purchase the

amount of the domestic industry’s good for one unit of the second

country’s currency.

Ball et al. (2010)

Export market

share (EMS) EMS can be measured in terms quantity or in terms of value.

Dosi, Grazzi & Moschella

(2013)

Net export index

(NEI)

An entity’s (nation, sector, industry or agribusiness) exports minus its

imports, divided by the total value of trade.

Banterle and Carraresi

(2007)

Grubel-Lloyd

measure (GLM)

Assesses the health of exports, by taking into account that a product is

often imported and exported simultaneously (known as intra-industry

trade).

Banterle and Carraresi

(2007)

Source: Adapted from Latruffe (2010) and based on Boonzaaier (2015)

3.1.2 Cost vs. benefit measures

Within this context, competitiveness is shown by way of performance indicators (Zairi,

1994), such as benchmarking (BFAP, 2012), cost superiority, profitability, productivity and

efficiency – which are often cited as measures or indicators of competitiveness (Spies, 1999;

Latruffe, 2010). The notion of opportunity costs is also attended to within these indicators or

measures (Gittinger, 1982:489; Freebairn, 1987:79). However, the most intuitive concept of

competitiveness is that of price competitiveness (Ball et al., 2010), which forms an integral

part of the reflexion on measures for competitiveness shown in Table 3.

Table 3. Summary of various cost vs. benefit measures for competitiveness

Measured Factor Measuring

Technique Description Author(s) applicable

Cost

Domestic resource

cost (DRC) ratio

Compares the opportunity costs9 of

domestic production with the value added it

generates.

Gorton et al. (2001) and Liefert

(2002)

Social cost-benefit

(SCB) ratio

The ratio of the sum of domestic (non-

tradeable) input cost to the price of the

considered product.

Masters and Winter-Nelson

(1995)

Agricultural costs of

production (ACP)

Itemised costs for various agricultural

activities.

Cesaro et al. (2008), Brunke et al.

(2009) and Omela and Värnika

(2009)

Profitability Gross margin The difference or ratio between incomes

and costs. Thorne (2004)

Productivity,

Efficiency and

Technological

Total factor

productivity (TFP)10

Ratio relating to the aggregation of input to

the aggregation of outputs. Coelli et al. (2005)

Mathematical

The measurement of the potential input

reduction, or output increase, relative to a

Farrel (1957), Aiger, Lovell and

8 Inputs and outputs

9 Gittinger (1982:489) describes opportunity cost as follows: “the benefit forgone by using a scarce resource for

one purpose instead of, for its next best alternative use”.

10 Also referred to as, and sometimes called, multi-factor productivity (MFP).

change

representation of

efficiency

reference with the construction of

efficiency frontier.11

Schmidt (1977); Charnes, Cooper

and Rhodes (1978) and Coelli et

al. (2005)

Index number

approach

Proposes explicit methods for the

aggregation12 of various inputs and outputs

to measure efficiency and technological

change.

Coelli et al. (2005)

Production function

estimation

Econometric estimation of a production

function.

Heady, Johnson and Hardin

(1956)

Malmquist indices

Provides a decomposition of the

productivity change into efficiency change

and technological change.

Caves, Christensen and Diewert

(1982)

Source: Adapted from Latruffe (2010) and based on Boonzaaier (2015)

3.1.3 Concluding remarks on measurements of competitiveness

Some of the measures and/or indicators for competitiveness may be viewed as somewhat

“restricted”, as not all of them are relevant to the definition proposed for competitiveness

relating to the highly contested international environment in which the South African stone

fruit industry operates, which depends highly on exports. Recent international and local

studies were reviewed to assess the applied measurements and competitive frameworks in an

agricultural context, taking forward the stepwise framework to evaluate competiveness for

this study.

The abovementioned measurement techniques and measures were taken into account and

considered to best fit the analyses of this research. The RTA measure was identified as the

most comprehensive measure in the context of this study, as

RTA calculations have the ability to compare the actual trade values of export

industries with each other within the same country; more importantly to compare the same export industries of different countries across the board; and also to include all relevant industries that would potentially attract the particular and relevant

scarce resources.

RTA measurement thus allow for “opportunity cost” (se definition above) to be factored

into the reason that the RTA measurement applies the concept of comprehensiveness and

relativity to all relevant factors.

3.3 Competitiveness analysis models

3.1.1 Porter’s diamond model of competitiveness

The Competitive Advantage Analysis, as described by Porter (1990), consists of examining

case studies of successful industries to identify why they are located in particular countries.

Porter, (2013:144) stated that: “We need a new perspective and new tools – an approach to

competitiveness that grows directly out of an analysis of international successful industries,

without regard to traditional ideology or current intellectual fashion. We need to know, very

11

The function that describes this frontier is unknown. Techniques for defining the frontier can be categorised

as non-parametric measures (data envelopment analysis – DEA), and parametric measures (stochastic frontier

model – SFM).

12 Several methods of aggregation lead to different TFP indices, e.g. Laspeyre, Paacshe, Fisher, Tornqvist and

Eltetö-Köves-Szulc (Latruffe, 2010).

simply, what works and why.” Porter (1990) studied 100 firms in ten developed nations to

learn if a nation’s prominence in an industry can be explained more adequately by variables

other than only the factors of production on which the theories of comparative advantage and

Heckscher-Ohlin model (Ohlin, 1933) are based (Cho & Moon, 2002).

According to Porter (1990: 1998), national prosperity is created, not inherited. It does not

grow from a country’s natural endowments (labour pool, interest rate or currency value), as

classical economics insists. A nation’s competitiveness depends on the capacity of its

industry to innovate and upgrade. Companies gain advantage against the world’s best

competitors because of pressure and challenge. Porter argues further that countries benefit

from having strong domestic rivals, aggressive home-based suppliers and demanding local

customers.

Companies achieve competitive advantage through acts of innovation that revolutionise the

business. Companies approach innovation in its broadest sense, including both new

technologies and new ways of doing business. They perceive a new basis for competing or

find better means for competing in old customs. Innovation can be manifested in a new

product design, a new production process, a new marketing approach or a new way of doing

business.

Esterhuizen (2006) asks three questions with regard to the competitiveness of a business:

Why are certain companies, based in certain nations, capable of consistent

innovation?

Why do they ruthlessly pursue improvements, seeking an ever more sophisticated

source of competitive advantage?

Why are they able to overcome the substantial barriers to change and innovation that

so often accompany success?

According to Porter (1990), the answer to the questions by Esterhuizen (2006) lies in four

broad attributes/determinants of a nation, attributes/determinants that individually and as a

system constitute the diamond of national advantage, the playing field that each nation

establishes and operates for its industries, namely factor conditions, demand conditions,

related and supporting industries, and firm strategy/structure and rivalry. These

attributes/determinants are illustrated in Figure 2. However, there are two added criteria or

attributes/determinants that also shape the environment in which firms compete and promote

the creation of a competitive advantage, namely government and chance.

Figure 2. Porter's diamond model of competitiveness

Source: Porter (1990; 1998)

The four attributes/determinants or criteria of the Porter diamond (Porter, 1990) can be

explained and described as follows:

Factor conditions. The nation’s position in the factors of production, such as skilled

labour or infrastructure, that are necessary to compete in a given industry.

Demand conditions. The nature of home-market demand for the industry’s products

or service.

Relating and supporting industries. The presence or absence in the nation of supplier

industries and other related industries that are internationally competitive.

Firm strategy, structure and rivalry. The way companies are created, organised and

managed, as well as the nature of domestic rivalry.

Each point on the diamond, and the diamond as a system, affect essential ingredients for

achieving international competitive success. The availability of the resources and skills

necessary for competitive advantage in an industry is critical. The information that shapes the

opportunities that companies perceive and the directions in which they arrange their resources

and skills is of the utmost importance (Fagerberg, Srholec & Knell, 2007). Porter (1990) add

these and also includes two outside variables (additional attributes or criteria - determinants)

in the model, which were mentioned above and circled in Figure 1, namely the role of chance

and the role of government.

Chance events are sporadic occurrences that have little to do with the circumstances in a

nation, and often are outside the power of firms and (sometimes) the national government to

influence. Examples include new inventions, major new technologies such as biotechnology,

and discontinuities in input costs such as the energy crisis, financial market shifts, foreign

government decisions, wars and changing weather patterns/conditions (Kandulu et al., 2012).

Firm strategy, structure

and rivalry

Related and supporting

industries

Demand conditions

Factor conditions

Government

Government Chance

Chance

The role of government is best viewed in terms of its influence on the four determinants of

competitiveness, rather than as a separate determinant per se. Porter explicitly rejects trade

intervention, arguing that it only guarantees markets for inefficient businesses (Porter, 1990).

Government has an indirect, rather than direct, role. Government plays a pivotal and enabling

role in the competitiveness of nations and industries alike, as argued extensively by

Acemoglu and Robinson (2013).

3.1.2 Institute for Management Development (IMD) - World Competitiveness Yearbook

The Institute for Management Development’s World Competitiveness Centre (WCC) has

been a forerunner in the field of the competitiveness of nations and enterprises since 1989

(WCC, 2011). The World Competitiveness Yearbook analyses and ranks how nations and

enterprises manage the totality of their competencies to achieve increased prosperity (WCC,

2011). It features 59 industrialised and developing countries, which are compared on 331

criteria which are grouped into four competitiveness factors in Figure 3. The hard data are

taken from international, national and regional organisations and private institutes, and

survey data are drawn from the annual Executive Opinion Survey of 4 935 respondents. A

fair measure of accuracy is achieved and maintained through its collaboration with 54 partner

institutes worldwide, and data are aggregated over a five-year period (WCC, 2011).

Figure 2 World competitiveness – Four-factor grouping

Source: IMD World Competitiveness Yearbook (WCC, 2011)

Figure 3. World competitiveness – Four factor grouping

Source: IMD World Competitiveness Yearbook (WCC, 2011)

WORLD

COMPETITIVENESS 1. ECONOMIC PERFORMANCE

1.1 Domestic economy

1.2 International trade 1.3 International investment

1.4 Employment

1.5 Prices

3. BUSINESS EFFICIENCY

3.1 Productivity and efficiency

3.2 Labour market 3.3 Finance

3.4 Management practices

3.5 Attitudes and values

2. GOVERNMENT EFFICIENCY

2.1 Public finance

2.2 Fiscal policy 2.3 Institutional framework

2.4 Business legislation

2.5 Social framework

4. INFRASTRUCTURE

4.1 Basic infrastructure

4.2 Technological infrastructure

4.3 Scientific infrastructure 4.4 Health and environment

4.5 Education

Prosperity

(Nations)

Profits

(Companies)

Standard of living

(People)

Regarding national competitiveness ratings, South Africa was ranked 52nd

for the year 2014,

a drop from 50th

position in 2012 (IMD, 2014).

3.1.3 World Economic Forum: Global Competitiveness Index

For the past three decades, the World Economic Forum’s annual Global Competitiveness

Reports have studied and benchmarked the many factors supporting national competitiveness.

From the start, the aim has been to provide insight into and stimulate discussion among

stakeholders on the best strategies and policies to assist nations to the overcome the

impediments to improving competitiveness (WEF, 2013). The GCI is a comprehensive tool

that measures the microeconomic and macroeconomic fundamentals of national

competitiveness.(WEF, 2013).The degree to which the factors, indirectly and directly, affect

the functioning and cohesion of one another has been captured within the GCI by including a

weighted average of many different components, each measuring a different aspect of

competitiveness. These components are accordingly grouped into 12 pillars of

competitiveness as shown in Figure 4. Within this assessment framework of national

competitiveness, South Africa was rated 56th

in 2014, dropping four places from the 2012

rating (WEF, 2014).

Figure 4. The 12 pillars of competitiveness

Source: WEF (2013)

The Porter diamond model of competitiveness is complemented by frameworks focusing on

the wider socio-economic and welfare rating of competitive performance, i.e. the frameworks

of the IMD World Competitiveness Yearbook (WCC, 2011) and the WEF annual Global

Competitiveness Report (WEF, 2013).

3.4 Some applications on competitiveness analysis in agriculture

There is a significant volume of recent studies focused on the competitiveness of the

agricultural industries or sectors. The important benchmark study by ISMEA (1999), which

piloted the approach of RTA and Porter’s diamond model to analyse competitive

GLOBAL COMPETITIVENESS

INDEX

Basic requirements

sub-index

1. Institutions

2. Infrastructure

3. Macroeconomic environment

4. Health and primary education

Efficiency enhancement

sub-index

5. Higher education and training 6. Goods market efficiency

7. Labour market efficiency

8. Financial market development 9. Technological readiness

10. Market size

Innovation and sophistication

factors sub-index

11. Business sophistication 12. Innovation

Key for

FACTOR-DRIVEN

Economies

Key for

EFFICIENCY-DRIVEN

Economies

Key for

INNOVATION-DRIVEN

Economies

performance of countries and industries in the expanding European Economic Union (EEC)

of the mid-nineties, must be viewed as the benchmark. In this study, two methods, namely

Porter's diamond model (1990) and competitiveness indicators as originally developed by

Balassa (1977), were prioritised to determine the competitiveness of the European Union

food chain in a global environment.

In Table 4, some other important international studies are listed. If notions can be singled out,

what is apparent throughout are the emphasis on trade; whether trade productivity is

enhanced, to trade better quality goods more efficiently; and the important role of agricultural

related policies, to improve trading and innovation in the depicted international

industries/sectors.

Table 4. Recent international agricultural competitiveness studies

National industry or

sector researched

Authors or

researchers

Proxies for

measurements and/or

models/frameworks applied

Verdicts or conclusions

Hungarian agricultural-

food sectors

Fertő and Hubbard

(2002)

RCA and RTA

Hungary has a comparative advantage for 11

of the 22 aggregated product groups.

Agricultural enterprises

in Slovakia

Bielik and Rajèániová

(2004)

Resource cost ratio (RCR)

Businesses and companies are more

competitive than co-operatives. The better the

soil quality, the more competitive these

businesses.

Namibian table grape

production Thomas (2007) Porter diamond model

The Namibian table grape chain is relatively

competitive in the international arena. Primary

production in becoming more competitive.

Milk production in

Ireland

Hopps and Maher

(2007)

Profitability and costs of

production (benchmarking)

Irish cash costs per litre are competitive in

Europe. Charges for owned land, capital and

family labour led to a lesser competitive

advantage.

Estonian milk

production

Omela and Värnika

(2009)

Opportunity cost approach;

domestic resource cost

Declining competitiveness of both small-scale

and large-scale producers.

Livestock product

exports from India Kumar (2010)

Export and import analysis –

nominal protection coefficient

(NPC)

India is competitive in the export of meat

products, except poultry.

China’s agricultural

products

Qiang, Yong-Sheng

and Xiao-Yuan

(2011)

RCA and

trade coefficient specialisation

(TCS)

Ability of direct factors is strong in terms of

transformation from cost advantage and price

advantage into competition advantage.

Poultry production in

the Czech Republic Belová et al. (2012)

Trade-related comparisons –

Lafay Index (LFI)

The comparative disadvantage deepens in

relation to European Union countries.

Global Pear Market Valenciano, Giancinti

and Uribe (2012) RCA

Geography plays a main role in

competitiveness with nearby markets, as

happens in markets with free trade.

Tobacco sub-sector in

the Republic of

Macedonia

Tuna, Georgiev and

Nacka (2013)

RCA and

Porter diamond model

The republic of Macedonia has favourable

conditions and a competitive advantage for

producing tobacco.

Orange juice chain in

Brazil

Neves, Trombin and

Kalaki (2013)

In-depth analysis of qualitative

fieldwork observations

The orange juice sector will probably not

realise the same future growth as other

important sectors of Brazilian agribusiness if a

few drastic steps are not taken.

Canadian wheat, beef

and pork sectors

Sarker and Ratnasena

(2014)

RCA and normalised revealed

comparative advantage

(NRCA)

Canada has enjoyed international

competitiveness in the wheat sector, but not in

the pork sector, whilst the beef sector has

grown rapidly since 1992.

Source: Adapted from Boonzaaier (2015)

The agricultural industries in South Africa were analysed with the application of various

measurement and different frameworks. There evidently is no absence of recent research

focused on the concept of competitiveness in the milieu of a deregulated South African

agricultural sector since the mid-1990s. Relevant publications in selected journals are shown

in Table 5. The research is listed chronologically, stating the authors, sector or industry,

alongside the proxies employed for measurements of competitiveness, as discussed above

with a brief conclusion.

Table 5. Summary of agricultural competitiveness studies related to South Africa

Authors and

Researchers Industry (sector)

Proxies for

measurements

Frameworks

applied

Verdicts or

Conclusions

Vink et al.

(1998)

Western Cape

wheat industry

Agricultural costs of

production

International and RSA

cost comparisons

Declining value of Rand provides

short-term relief and production

practices need to be adapted.

Venter and

Horsthemke,

(1999)

Southern African

sheep meat

industry

Profitability and costs

of production

Porter’s diamond

model framework

Industry/value chain is less

competitive than that of Australia.

Blignaut

(1999)

RSA dairy

industry

RCA

Low cost and

differentiation

comparisons

Porter five competitive

factors13

Not internationally competitive,

but primary milk producers are

relatively effective.

Esterhuizen

and

Van Rooyen

(1999)

RSA food

commodity chain RTA

Porter diamond model

framework

16 selected food commodity

chains. Majority of chains are

marginally competitive, except for

the maize, pineapple and apple

chains. Index decreases when

moving from primary to processed

products.

Du Toit

(2000)

RSA apple

industry

Comparative

analysis: Production

and related costs

Porter’s diamond

model framework RSA less competitive than Chile.

Jooste and

Van

Schalkwyk

(2001)

RSA primary

oilseeds industry DRC ratio

Comparisons of

economic advantages

(CEA)

Where agro-ecological conditions

are poor – improved-yield cultivars

will determine comparative

advantage. Distortionary policies

on input side are a main factor

influencing comparative advantage.

Van Rooyen

et al. (2001)

RSA flower

industry

RCA,

DRC and

private cost ratio

(PCR)14

Porter diamond model

framework

and

PAM

Overall, RSA has a competitive

advantage over Australia, except

when using the Porter analysis,

where certain determinants are

stronger, i.e. government support

13

Five competitive forces: 1 – The entry of new competitors, 2 – Bargaining power of suppliers, 3– Bargaining

power of buyers, 4 – Threat of substitutes, 5– Rivalry among the existing competitors (Porter, 1985).

Authors and

Researchers Industry (sector)

Proxies for

measurements

Frameworks

applied

Verdicts or

Conclusions

and the role of chance determinant.

Kalaba and

Henneberry

(2001)

Fruits (grapes and

pome fruit) in the

EU market

Trade-based models:

Import demand

model [Restricted

source-differentiated

almost ideal demand

system (RSDAIDS)]

Trade-related

comparisons

RSA fruit exports are least

competitive among Chile, the

United States, Argentina and

Turkey.

Esterhuizen,

Van Rooyen

and Van Zyl

(2001)

RSA agricultural

input industry RTA

Trade-related

comparisons

RSA manufacturing of farming

requisites is relatively marginally

competitive. Competitiveness of

machinery industry is improving.

Fertiliser industry is becoming

more competitive.

Pesticide industry is decreasing in

its competitiveness.

Mahlanza et

al. (2003)

Organic wheat

production in

Western Cape

SCB and DRC ratios PAM

Weak comparative advantage for

conventional wheat in WC, except

for certain areas of Swartland.

Would be an improvement if wheat

could be produced under organic

practices.

Mosoma

(2004)

RSA agricultural

exports RTA

Trade-related

comparisons

Marginally competitive

internationally.

Hallatt (2005) RSA oilseed

industry RCA, NXI and RTA

Trade-related

comparisons

RSA secondary oilseed industry is

struggling with a competitive

disadvantage against Argentina,

whilst primary industry is more

competitive than Argentina.

Esterhuizen

and

Van Rooyen

(2006)

RSA wine

industry RTA GCR (WEF)

Industry enjoys a sustained

improvement in competitiveness.

Mashabela

and Vink

(2008)

RSA deciduous

fruit supply chains RTA

Trade-related

comparisons

RSA enjoys a relative global

competitive advantage. Increased

competitiveness further up the

chain.

Van Rooyen

(2008)

RSA agribusiness

sector RTA

Porter diamond model

framework

Can be classified as generally

marginal in terms of global

competitiveness.

Madima

(2009)

RSA deciduous

fruit canning

industry

RTA Porter diamond model

framework

Industry is internationally

competitive in the following areas:

labour costs, product quality,

efficient production technology and

regulatory standards.

Beukes (2009) RSA apple

industry

Production

efficiency, industry

and infrastructure

inputs, financial and

market factors

O’Rourke framework

RSA less competitive than Chile

and New Zealand. Production is

area of best competitive

performance for RSA.

Ndou and Obi

(2011)

RSA citrus

industry

Constant market

share (CMS)

Porter diamond model

framework Industry is competitive

14

Private cost ratio (PCR) is a measure to compare the competitiveness of different systems with one another

(Van Rooyen et al., 2001).

Authors and

Researchers Industry (sector)

Proxies for

measurements

Frameworks

applied

Verdicts or

Conclusions

Dennis (2011) RSA sunflower

industry RCA and RTA

Porter diamond model

framework

Value-added sunflower products

struggle with a competitive

disadvantage.

Van Rooyen

et al., (2011)

RSA wine

industry RTA

Porter diamond model

framework

RSA wines are increasingly

internationally competitive, with a

positive trend since 1990s.

Van Rooyen

and

Esterhuizen,

(2012)

RSA agribusiness

sector RTA

Porter diamond model

framework

The sector is marginally

competitive, but constrained by an

increasingly negative trend since

2004.

Jafta (2014) RSA apple

Industry RCA and RTA

Porter diamond model

framework

RSA apple industry is marginally

competitive in the international

market.

Boonzaaier

(2015)

RSA stone fruit

industry RTA

Porter Diamond

model, WEF – Global

Competitiveness Index

and IMD – World

Competitiveness

Yearbook

The industry clearly is highly

competitive in the global trading

arena and performed so on a

sustainable basis, especially since

the period of deregulation in the

mid 1990’s with Plums was the

most competitive stone fruit type,

followed by apricots, peaches and

nectarines, and lastly cherries.

Angala (2015) Namibian date

industry RTA Porter Diamond model

Namibian date industry is

competitive in the international

market.

Source: Adapted from Boonzaaier (2015)

The application of trade-based measures as proxies for competitiveness is clearly evident,

where the RTA measure, noticeably in conjunction with the Porter diamond model of

competitiveness, dominates as analytical framework and also to mobilise expert views and

opinions to assess the competitive performances.

Results and major findings: 4.

4.1 Measuring the competitive status and performance (Step 2 of the analytical framework)

The competitiveness performance of the South African stone fruit industry was firstly

calculated using the FAOSTAT15

data base (Figure 5). Here it must be noted the FAOSTAT

only include agricultural commodities and the RTA formula therefore provides a less

comprehensive view of “opportunity costs” as non-agricultural trade is not included. The

advantage of FAOSTAT however is that it is available from 1961 and covers the deregulation

period, the nineties, of SA agriculture.

15

Food and Agricultural Organisation Statistical Database (FAO, 2014)

Figure 5. Phases of competitive performance of SA stone fruit industry

Source: Based on Boonzaaier (2015)

Secondly, the ITC16

data, available from 2001, provide a more comprehensive view as non-

agricultural commodities are also captured in the calculation based on the database provided

by International Trade Centre (in Figure 5).

RTA values above 1 calculated from 1961 to 2012, indicate that this industry is competitive.

When the two respective datasets of the FAO (2014) and ITC (2014) are depicted for the

period from 2001 to 2011, the period for which corresponding RTA calculations from both

datasets would be available, both trends follow the same movement, but with varying

magnitude as shown in Figure 5. This also indicates that the SA stone fruit industry measures

as being marginally more competitive within the multi-economic sector index, viz.

agriculture-based competitiveness.

It can be concluded that there is a ‘relatively’ more intense competition between agricultural

products/commodities. These products/commodities compete for a common set of resources

in a more ‘sticky resource mobility’ environment, i.e. the major competing internationally

traded alternatives are found within the direct agricultural production alternatives to stone

fruit, such as other deciduous fruit – apples, pears and grapes, citrus, exotic fruits and

vegetable groups. Within an only ‘agricultural environment’, stone fruit thus are considered

somewhat less competitive, while this industry outcompetes many more industries when non-

agricultural alternatives are considered in the measuring process. Difference between the

respective RTA dataset calculations were not analysed in depth, however, and further

research is needed to express a broader statement on these results, i.e. varying measurements.

Trends and phases: Five phases were identified (in collaboration with industry experts) in the

competitive performance of the South African stone fruit industry since 1961, showing the

generally competitive but fluctuating nature of the performance of this industry:

Phase I (1961-1982): Increasingly regulated competitiveness(RTA values from

0.41 to 2.19)

Phase II (1983-1990): Politically constrained competitiveness (RTA values from

2.50 to 2.19)

Phase III (1991-1999): Economic deregulation and internal rivalry(RTA values

from 2.39 to 5.61)

16

International Trade Centre (Trademap) database (ITC, 2014)

0

1

2

3

4

5

6

FOASTAT ITC

Phase I Phase II Phase III Phase V Phase IV

Phase IV (1999-2007): Towards international competitiveness (RTA values from

5.61 to 2.97)

Phase V (2007 – present): Increasingly sustained competitiveness (RTA values >

2.66)

Comparisons with other fruit types: When stone fruit is compared to other South African

(SA) horticultural crops, plums consistently claim the top position as shown in Figure 6.

Figure 6. RTA comparisons of South African deciduous fruits

Source: Boonzaaier (2015)

The South African plum industry outranks all competitors in the RSA deciduous fruit basket.

As of 1997 (post-deregulation), the performance of plums has been on a par with that of

pears, but since 2008 plums outrank all other deciduous fruit types in both the agricultural

index (FAOSTAT data) and the multi-sector index (ITC data).

The RSA stone fruit industry furthermore competes on a lower level than the pome fruit and

table grape industry, but still outranks fruit industries such as the tropical fruit, exotic fruit

and nut industries, and operates on par with the SA wine industry.

International comparisons: In the international field of stone fruit competitiveness, the SA

industry is the runner-up in the first league behind Chile – the clear leader in this

environment. As stone fruit form part of the deciduous fruit grouping within the larger fruit

and nut grouping, SA can be considered as a major competitor in this broad set of industries,

not only in the SH, but also in NH countries as shown in Figure 7.

Figure 7. International stone fruit RTA comparisons

Source: Boonzaaier (2015)

-5.00

0.00

5.00

10.00

15.00

20.00

25.00

Plums & Sloes Pears & Quinces Grapes Apples

Wine Apricots Peaches & Nectarines Cherries

0.00

1.00

2.00

3.00

4.00

5.00

6.00

SA ARG AUS NZ FRC ITA

4.2 Identifying the competitiveness factors (Step 3of the analytical framework)

Through an industry wide survey - the Stone Fruit Executive Survey (SFES) - views and

opinions of prominent industry role-players were mobilised through the SFES (53

questionnaires) and also through focused group sessions, organised by relevant industry

bodies. Trends and phases, described in the section above were firstly considered. The current

performance trends were secondly attended to. A total of 84 factors affecting the

competitiveness of the industry were identified, and these were rated on a five-point Likert

scale (where 5 were most enhancing and 1 was most constraining). Average based analysis

are explored and elaborated by the following statistical methods to differentiate between the

functional positions in the value chain.

Value chain analysis: In order to consider possible differences in views on factors affecting

competitiveness between the functional role players in the value chain, representative

groupings or opinion clusters were identified. This will allow the identification of the

respective scores of the most agreed-to statements and enable clustering of opinions in terms

of significance, i.e. to sketch a more realistic/accurate picture of the competitiveness and

address issues and opportunities at hand to complement the empirical measurement.

Cluster analysis was carried out to determine whether significant opinion groupings exist and

whether these correlate with different functions in the value chain, i.e. functional opinion

cluster analysis. As discussed in the findings by Boonzaaier (2015) where the percentages for

Cluster 1, Cluster 2 and the General industry refers to the corresponding share(%) for each

item/activity/type/form in the row. To test whether the average rating scores or statements

differed significantly for different role players in the value chain and also in relation to the

size of the business, ANOVA (analyses of variance) was employed (Keller & Warrack,

2000).

Principle component analysis (PCA) was applied to identify redundant (highly correlated)

variables, i.e. factor ratings in the data set for which individual responses were very similar/

concentrated – to be viewed as consensus factors, as well as uncorrelated variables, i.e.

factors to which respondents gave a more variable range of rating values – to be viewed as

variation factors (Wold et al., 1987). The objective of this analysis was to yield a dataset

containing information to ease strategic planning processes, i.e. to differentiate between

variation and consensus factors as basis for industry level discussions and actions

(Boonzaaier, 2015).

A statistically significant difference (p < 0.05) is expressed in terms of the following;

Cluster 1 (Agribusiness orientated) contains a larger share of role players who are

‘input suppliers only’, ‘exporter/marketer only’, as well as ‘producer and pack

house/processor’ and ‘exporter/marketer’.

Cluster 2 (Producer orientated) contains a larger share of role players who are

‘producers only’, as well as ‘producers and pack house/processors combined’.

The top 10 enhancing and top 10 constraining factors for both cluster and the average

industry score are shown in Table 7.

Table 7. Most enhancing and most constraining factors for the two clusters

CLUSTER 1: Agribusiness orientated

Top 10 enhancing factors

Average

score

cluster 1

Top 10 constraining factors

Average

score

cluster 1

International market competition 4.32 Politicians’ trustworthiness 1.54

Technology services 4.25 Political system credibility 1.68

Reinvestment 4.18 Entry-level labour: Quality 1.76

Exchange rate 4.14 Labour policy 2.04

Local input suppliers: Availability 4.12 Land reform policy 1.96

General infrastructure 4.11 Political system 2.07

Economies of scale 4.11 Social unrest 2.12

Location suitability 4.08 Establishment and production cost 2.15

Storage/product handling: Facility availability 4.08 Skilled labour: Obtaining 2.22

Relation 4.07 Crime 2.30

CLUSTER 2: Primary producer orientated

Top 10 enhancing factors

Average

score

cluster 2

Top 10 constraining factors

Average

score

cluster 2

International market competition 4.10 Politicians’ trustworthiness 1.35

Economies of scale 4.00 Political system credibility 1.30

General infrastructure 3.85 Entry-level labour: Quality 1.35

Location suitability 3.79 Labour policy 1.40

Exchange rate 3.70 Land reform policy 1.71

Entry-level labour: Obtaining 3.65 Political system 1.65

Technology services 3.55 Social unrest 1.79

Storage/product handling: Facility availability 3.45 Establishment and production cost 1.75

Local market competition 3.35 Skilled labour: Obtaining 1.70

Technology quality 3.25 Crime 1.65

Source: Boonzaaier (2015)

Figure 8. Rating of factors influencing the stone fruit industry’s competitive performance

Source: Boonzaaier (2015)

Notes: Cluster 1 =Agribusiness orientated; Cluster 2 =Producer orientated; General industry =Industry average

Scores/ratings: 0 = most constraining, 3 = neutral rating, and 5 = most enhancing

0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00

Politicians' Trustworthiness

Political System Credibility

Entry-level labour: Quality

Labour Policy

Land Reform Policy

Political System

Social Unrest

Establishment-and Production cost

Skilled labour: Obtaining

Crime

Buying Patterns

Government Supported Research

Corruption

Growth

Technology Cost

SA Climate Variation

Skilled labour: Competency

Entry-level labour: Cost

Local Market

Consumer Education

Technology Services: Cost

Skilled labour: Cost

Local Consumers

Infrastructure Cost

BEE Policy

Taxation

Trade Policy

Administrative Regulations

Transaction Cost

Political Factors

Electricity

Storage/Product Handling: Facility Cost

Health

Competition Law

Macro-economic Policy

World Events

Grower-club access

Economic Growth/Development

Credit: Long-term

Natural Resource Access

Expenditure R&D

Exchange rate Fluctuations

Seasonality and Availability

Telecommunication

Credit: Short-term

Finacial Services

Private Research

Evaluation

Diversity

New Competitors

Resource Base

Ability to Utilise unfavourable conditions

R&D Collaboration

Characteristics

Regulatory Standards Comply

Export Infrastructure

Information flow: Customers

International Market

Research Quality

Regulatory Standards

Market Intelligence Mangement

Information flow: Primary Suppliers

Efficiency

Productivity

Quality Technology Access

Local Input Suppliers: Sustainability

Resource Competition

Technology Quality

Cold-chain Management

Local Market Competition

Effectivity

Transport

Local Input Suppliers: Quality

Relation

Reinvestment

Local Input Suppliers: Availability

Entry-level labour: Obtaining

Storage/Product Handling: Facility Avialability

Location Suitability

Technology Services

Exchange Rate

General Infrastructure

Economies of Scale

International Market Competition

General

Industry

Cluster 2

Cluster 1

4.3 Identifying and analysing the Porter diamond determinants (Step 4 of the analytical

framework)

Step 4 follows Step 3, as it encapsulates and categorises sets of factors identified by the

industry stakeholders into constellations or clusters of determinants for competitiveness. The

model developed by Porter (1990) was applied and the captured information from the SFES

was interpreted within the six broad competitiveness determinants or attributes within which

an industry operates (Porter, 1990; 1998).

Analysing the 84 factors of competitiveness within the framework of the Porter diamond

model of competitiveness first required the grouping of these factors as sets into production

factor conditions; demand/market factors; relating and supporting industries; firm-level

business strategy structure and rivalry; government support and policy; and chance of

opportunity factors.

The respective SFES scores for the factor sets grouped into each determinant (aggregated out

of five for each determinant) were calculated. A Likert-scale (Likert, 1932) was applied and a

score closer to 5 represents more enhancing impact on competitive performance, whereas a

score closer to 1 represents a more constraining impact on competitive performance for the

purpose of the study. With this method, each Porter determinant carries an equal weight – out

of five ratings for each determinant. The determining of realistic weightings was not possible

from the SFES. Step 5 however dealt with this aspect to some degree.

The rated factors were grouped into Porter’s six determinants and the general industry (see

Figure 9) scored ratings yielded the two most enhancing determinants, being business

strategy, structure and rivalry (3.55 out of 5) and related and supporting industries (3.14 out

of 5). Production factor conditions (2.81 out of 5) and demand/market factors (2.76 out of 5)

were identified as being less enhancing determinants. Chance factors (2.66 out of 5) and

government support and policy (2.35 out of 5) were identified as the two most constraining

determinants.

Figure 9. Porter determinants of competitiveness: Comparing clusters

Source: Boonzaaier (2015)

Differences in the views of role players were again considered through a cluster analysis,

dealing with role players who are ‘input suppliers the agribusiness orientated cluster (cluster

00.5

11.5

22.5

33.5

4

Business Strategy, Structure and

Rivalry

Relating and Supporting Industries

Production Factor Conditions

Demand Market Factors

Chance of Opportunity Factors

Government Support and Policy

Cluster 1

Cluster 2

General

Industry

1) and a producer orientated cluster (cluster 2) General industry refers to the combined

(entire) stone fruit responses irrespective of the functional value chain position claimed.

4.4 Value chain differences

As different cluster groupings based on functional value chain positions were analysed, it

became clear that there were significant differences between the respondents involved in the

primary production and packing of stone fruit and the respondents involved in activities

lower down the value chain, such as in pack houses/processors and exporters/marketers.

Further down the value chain (processing, trading), the respondents (relating to Cluster 1)

expressed more “bullish” or optimistic views and positive statements on competitiveness than

those directly exposed to primary production (relating to Cluster 2) risks and uncertainties.

This confirms the importance to ensure alignment re competitive performance to all related

functions in the value chain i.e. to expand competitive analysis to cover different points in the

value chain in order to create better strategic alignment.

4.5 Analysing the individual Porter diamond determinants

A next phase in a comprehensive analysis of factors affecting competitive performance

entails the analysis of separate determinants with their related factors. In this paper only the

factors within the “firm strategy, structure and rivalry” determinant is shown, as it deals with

value chain considerations (Fig 10). This analysis is the result of focus group discussions

with relevant industry players for each determinant set.

Figure 10. Firm strategy, structure and rivalry determinant of competitiveness

Source: Boonzaaier (2015)

A high degree of consensus was found through PCA of the factor, resource base for projected

stone fruit operations is sufficient, as expressed by the respondents, but Cluster 1

(agribusiness role players) was keener to reinvest in stone fruit operations as their resource

base was even more aligned to facilitate projected stone fruit operations. Additionally,

Cluster 1 expressed more positive views on the factors and synergies regarding the flow of

0.00

1.00

2.00

3.00

4.00

5.00

Information flow: Primary

Suppliers

Information flow: Customers

Market Intelligence Management

Local Market Competition

New Competitors

International Market

Competition

Economies of Scale

Reinvestment

Resource Base

Resource Competition

Cluster

1

Cluster

2

Industry

Average

information and market intelligence. New competition is viewed as less enhancing to the

competitive performance of the industry – in contrast to the view of Porter that the more

competitors the better. This is understandable as ‘own views’, because firms, industries and

nations are constantly competing for resources and “uncontended market space” on the

perception that market share will decrease when new competitors enter the ‘game’.

For this determinant to enhance the competitive performance of this industry, it is necessary

that the flow of information and the management thereof are adequate and available in the

desired format for all stakeholders, whilst not neglecting other enhancing factors.

As the relative position and involvement in the value chain presents significant statistical

differences, it is expected that conglomerations between smaller role players to integrate

vertically into the value chain possibly would yield competitive performance enhancements.

Consolidations, or perhaps improved value chain management functions, between industry

role players and other stakeholder will be essential to improve not only the competitiveness

of individual firms in this regard, but also of the industry as a whole. Evidence of such

consolidations can indeed be found in the industry.

4.5 Validating the Porter diamond model

This study applied the SFES to additional two frameworks to ensure that the required re-

grouping of factors (from the Porter diamond determinant groupings) is meaningful. This was

done through the Cronbach’s alpha test (Cronbach, 1951). Cronbach’s alpha was firstly

employed to test the consistency of responses regarding the SFES statements/factors that

were restructured to fit the frameworks of the IMD (World Competitiveness Yearbook -

WCY) and the WEF (Global Competitiveness Report - GCR). The responses in their own

right provide a high level of consistency – proving that the 84 questions in the SFES are

validated as highly relevant and the application of the Porter diamond model is substantiated.

Boonzaaier (2015) portrayed the findings expressively.

For the IMD WCY the factor referring to government attributes is viewed as less enhancing,

whilst the factors of infrastructure and business efficiency are perceived as more enhancing.

The factor economic performance is perceived as most enhancing to the competitive

performance of the SA stone fruit industry. In context of WEF results, the pillar, institutions,

is viewed as most constraining, whilst labour efficiency, market size, macroeconomic

environment, goods and market efficiency, innovation and financial market development are

viewed as more enhancing pillars. Business sophistication, technological readiness and

infrastructure are viewed as the most enhancing pillars within this framework for the

competitive performance analysis of the South African stone fruit industry (Boonzaaier,

2015)

Furthermore, if the SA stone fruit industry was analysed according to the WEF GCR in terms

of what drives an economy,17

it can be best described as “innovation driven”, as it calculated

a score of 3.32 within the respective model. A “factor-driven” economy yielded a lower score

of 2.49, and an “efficiency-driven” economy a score of 2.84. This also correlates with the

high-scoring determinants of the Porter diamond, viz. business strategy, structure and rivalry;

relating and supporting industries; and production factor conditions (Boonzaaier, 2015).

17

In the terms of this study, an industry instead of an “economy” (Boonzaaier, 2015)

4.6 Strategies to enhance the competitive performance (Step 5 of the analytical framework)

The above results and major findings were interrogated through industry wide work sessions

and focus group discussions eleven major industry level strategies were agreed upon. In

doing this the notion of “equal weighting” in the Porter diamond determinants were

somewhat countered. These priority actions are shown in Table 8 and are related to the Porter

diamond model.

Table 8. Priority industry level strategies to improve competitive performance.

Porter

determinants

Relevant and

constraining

competitive

factors

Strategic proposals

Production

factors

conditions

Technology cost

1. Technological innovation through value chain collaboration: Upgrade

and expansion of stone fruit technological innovation platforms to focus

attention on aspects impacting on competitiveness in global markets; to

encourage a long term vision; to foster investments in technological

innovations through public-private initiatives; to broaden the scope and

extent of technology affordability; collaborative information management

sharing between stakeholders and clients along the value chain network;

focus on “smart fresh” (new cooling technology), climate and moisture

management tools, fruit-handling systems, fruit thinning and harvesting

platforms, chemicals/fertilisers application equipment, etc.

2. “Anticipating climate change”: The tracking and projection of possible

climate variation conditions and possible impacts, like; heat waves prior to

harvesting of fruit, frost damage during the flowering period of fruit,

projected chilling unit (Richardson units and ARC units) accumulation

measurement, shifting periods of full-bloom to harvesting, role of insects

(pests and bees/natural predators of pests), virus infections and diseases, etc.

Demand/

market

factors

Inconsistent

quality and

availability of

SA stone fruit

varieties in

markets

3. Improved consistency in supply to exports markets: Market access are

constrained by the inconsistency of fruit/cultivar types/tastes availability.

The grouping of varieties with similar attributes and qualities as

“homogenous products” must be considered to maintain/ensure product

continuity. Quality control will also minimise “product confusion”.

4. Extended supply in export markets: Market access will also be improved

by breeding and evaluation (on a continuous and sustainable basis) of

cultivars/varieties for specific production regions to lengthen and “even out

the spikes” of fruit supplied in the global market(refer to strategic proposal

1)

5. Market intelligence to achieve preferred supplier status: Create market

intelligence by linking consumer profiling in international markets to

innovations in storage and ripening of fruit and to national cultivar breeding

and evaluation programmes; and as such to claim the status of preferred

suppliers in international markets.

Chance factors

The influence of

adverse weather

conditions on

buying patterns

of consumers

(export markets)

6. Redirecting market supply: Buying patterns are often negatively impacted

on by ad hoc and unexpected adverse weather conditions, such as heavy

snow falls negatively affecting infrastructure network and logistics in export

markets. Therefore contingency plans to proactively anticipate such

conditions through ”early warning systems” together with collective action

from producers, exporters, overseas clients related to alternative

destinations and inventories.

Related and

supporting

industries

Electricity

supply

(including

renewable

energy and fossil

fuels)

7. Consistency of power supply: As stone fruit are extremely susceptible to

“break-ups/stoppages” in the cold-chain, inconsistent electricity supply in

the will have to be addressed in a much improved manner through area/time

targeting and “early warning systems” with government departments;

(Energy; Trade and Industry; Science and Technology) and agencies such as

ESCOM (Electricity Supply Commission of South Africa). Investment in the

provision of additional/supplementary electricity supply initiatives,

especially during periods of critical demand also need to be considered, inter

alia through renewable and fossil fuels options.

Industry’s

expenditure on

Research

&Development

and innovation

8. Institutional arrangements to create innovation through collaborative

partnerships: Well-structured public-private Research & Development

partnerships ( for example between ARC – Agricultural Research Council,

CSIR – Council for Scientific and Industrial Research and selected

industries) to collaborate on the development of innovation through:

Goal driven research objectives and outputs; impact and cost-benefit

analyses; effective management of budgets and resource allocation to

priority projects.

Industry levies refocussed to improve the systems impacting on competitive

performance.

Government

support and

policy

Trade policy

9. Trade promotion support: Trade promotion negotiations and industry

lobbying with relevant government departments to achieve/gain market

access and realise international trade agreements into potential lucrative

markets such as China and India; and inclusion in trade missions and trade

agreements.

Dealing with the

political

economy

10. A “Stone Fruit Industry Plan (SFIP) and compact: The establishment of

a compact between industry and government to restore mutual trust and to

create a “Shared-mission, joint-vision and strategic plan” (a Stone Fruit

Industry Plan) for the industry by all role players and affected stakeholders.

The SFIP should aim to establish an agreed to and transparent framework of

agreement and co-operation with checks and balances to create conjoint

engagement and governance to align major stakeholders and combat

negatives such as corruption, discrimination, favouritism, racism, etc. at all

levels. Private: public partnership, referred to above, will be an important

component of this SFIP, including such collaboration on transformation and

land redistribution matters.

11. Improved industry intelligence systems: High quality and improved

industry intelligence will enable improved co-operation, lobby and

negotiation at all levels, dealing with matters related to:

Human capital factors (including labour) and societal issues.

Education, capacity and training programmes.

Investment environment that the industry faces – improving the “climate”

for South African agriculture and more specifically stone fruit.

Articulate the role and impact of the stone fruit industry in the broader

economy, relating to the stimulation of employment opportunities and

income generation.

Source: Boonzaaier (2015)

The table above does not relate to the Firm Level determinant, as this was not discussed at

industry level. However some of the priority statements above clearly impacts on such firm

level approaches required to enhance competitive performance.

The major strategic improvements to enhance competitive performance argued for focus on

improved industry-based lobby discussions, i.e. to build and strengthen the necessary

communication between industry role players and government agencies through an improved

strategic intelligence database, centred on aspects such as trade agreements, international

market development and policy development.

Conclusions 5.

The major findings of this study established that the South African stone fruit industry

performs highly competitive, albeit somewhat fluctuating, in the global trading arena on a

sustainable basis; especially since the period of deregulation in the mid 1990’s. Plums was

the most competitive stone fruit type, followed by apricots, peaches and nectarines, and lastly

cherries. The competitive performance of these individual stone fruit types increased

significantly from the late 1990s onwards, showing gradual decreases in the early 2000s, but

recovered from 2007 onwards. From the analysis 84 factors impacting on competitive

performance were identified and analysed in collaboration with industry role players. From

this eleven priority industry-based strategic actions were, reflecting aspects for improved

strategic intelligence, value chain coordination, government interaction and policy

development were formulated for consideration in a comprehensive stone fruit industry

strategy plan.

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